Count-Based Exploration in Feature Space for Reinforcement Learning

Understand

We introduce a new count-based optimistic exploration algorithm for Reinforcement Learning (RL) that is feasible in environments with high-dimensional state-action spaces.

• The success of RL algorithms in these domains depends crucially on generalisation from limited training experience.
• Function approximation techniques enable RL agents to generalise in order to estimate the value of unvisited states, but at present few methods enable generalisation regarding uncertainty.
• This has prevented the combination of scalable RL algorithms with efficient exploration strategies that drive the agent to reduce its uncertainty.